[SciPy-User] Select rows according to cell value
Juan Luis Cano Rodríguez
juanlu001 at gmail.com
Tue Nov 13 12:41:46 EST 2012
Actually I arrived to a couple of one-liners:
d = np.take(data, [np.argwhere(data[:, 0] == a).flatten()[0] for a in
altitudes], axis=0)
or
d = np.array([data[data[:, 0] == a][0] for a in altitudes])
I find them sort of ugly but maybe it's the only way. The same way I'd do
data[[1, 3, 8]]
to retrieve the first, third and eighth I'd like to do
data[np.magic_indices(altitudes)]
On Tue, Nov 13, 2012 at 6:02 PM, Oleksandr Huziy <guziy.sasha at gmail.com>wrote:
> Yeps, I admit with pandas it appears much easier
>
> import pandas
> df = pandas.read_csv("tmp/data.txt", sep="\\s")
> df = df.dropna(axis = 1)
>
> #df.index = df["alt"]
> selection = df.select(lambda i: df.ix[i, "alt"] in altitudes)
> print selection
>
>
> cheers
> --
> Oleksandr (Sasha) Huziy
>
>
>
> 2012/11/13 Oleksandr Huziy <guziy.sasha at gmail.com>
>
>> I am not sure if this way is easier thsn yours, but here is what I wpuld
>> do
>>
>> tol = 0.01
>> all_alts = data[:,0]
>> print all_alts
>> all_alts_temp = np.vstack([all_alts]*len(altitudes))
>> print all_alts_temp
>>
>> sel_alts_temp = np.vstack([altitudes]*len(all_alts)).transpose()
>> print sel_alts_temp
>> sel_pattern = np.any( np.abs(all_alts_temp - sel_alts_temp) < tol, axis =
>> 0)
>> print sel_pattern
>> print data
>> print data[sel_pattern,:]
>>
>>
>> Cheers
>> --
>> Oleksandr (Sasha) Huziy
>>
>>
>>
>>
>> 2012/11/13 Andreas Hilboll <lists at hilboll.de>
>>
>>> Am Di 13 Nov 2012 17:07:19 CET schrieb Juan Luis Cano Rodríguez:
>>> > I am loading some tabular data of the form
>>> >
>>> > alt temp press dens
>>> > 10.0 223.3 26500 0.414
>>> > 10.5 220.0 24540 0.389
>>> > 11.0 216.8 22700 0.365
>>> > 11.5 216.7 20985 0.337
>>> > 12.0 216.7 19399 0.312
>>> > 12.5 216.7 17934 0.288
>>> > 13.0 216.7 16579 0.267
>>> > 13.5 216.7 15328 0.246
>>> > 14.0 216.7 14170 0.228
>>> >
>>> > into an ordinary NumPy array using np.loadtxt. I would like though to
>>> > select the rows according to the altitude level, that is:
>>> >
>>> > >>> data = np.loadtxt('data.txt', skiprows=1)
>>> > >>> altitudes = [10.5, 11.5, 14.0]
>>> > >>> d = ... # some simple syntax involving data and altitudes
>>> > >>> d
>>> > 10.5 220.0 24540 0.389
>>> > 11.5 216.7 20985 0.337
>>> > 14.0 216.7 14170 0.228
>>> >
>>> > I have tried a cumbersome expression which traverses all the array,
>>> > then uses a list comprehension, converts to an array... but I'm sure
>>> > there must be a simpler way. I've also looked at argwhere. Or maybe I
>>> > should use pandas?
>>> >
>>> > Thank you in advance.
>>> >
>>> >
>>> > _______________________________________________
>>> > SciPy-User mailing list
>>> > SciPy-User at scipy.org
>>> > http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>> +1 for using pandas
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>>
>>
>
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